# Hybrid Machine Learning Forecasts for the FIFA Women's World Cup 2019

arXiv: https://arxiv.org/abs/1906.01131 · HTML: https://mlsanity.com/paper/1906.01131/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 2 of 2)

- [Hybrid Machine Learning Forecasts for the UEFA EURO 2020](https://mlsanity.com/paper/2106.05799/) (2021)
- [Modeling and Prediction of the UEFA EURO 2024 via Combined Statistical Learning Approaches](https://mlsanity.com/paper/2410.09068/) (2024)

## Abstract (arXiv)

In this work, we combine two different ranking methods together with several other predictors in a joint random forest approach for the scores of soccer matches. The first ranking method is based on the bookmaker consensus, the second ranking method estimates adequate ability parameters that reflect the current strength of the teams best. The proposed combined approach is then applied to the data from the two previous FIFA Women's World Cups 2011 and 2015. Finally, based on the resulting estimates, the FIFA Women's World Cup 2019 is simulated repeatedly and winning probabilities are obtained for all teams. The model clearly favors the defending champion USA before the host France.